denotes a weighting function based on trainable mean vector \(\mu _{k}\)  and diagonal covariance matrix \(\Sigma _{k}^{-1}\) . Such a layer involves three types of trainable weights: \(\Theta _{k} \in \mathbb {R}^{\widetilde{L} \times L}\)  maps input \(\textbf {u}_{i}^{j}\)  to the new dimensionality, and \(\mu _{k}\)  and \(\Sigma _{k}\)  define the Gaussian kernel used to convolve a node with its neighbors. Our final model consists of 25 GNN layers with ELU nonlinearities [1]} and skip connections [2]}.
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